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Thermal-load-sensitivity quantities (z, CTmax) with confidence intervals

Source: R/tls.R
tls.Rd

The frequentist analogue of bayesTLS::tls(). Reads a fit_4pl() (freq_tls) fit and returns the headline thermal-load-sensitivity quantities — thermal sensitivity z and CTmax — as point estimates with confidence intervals, one row per group when the fit is grouped. Uncertainty uses the engine's profile-likelihood intervals by default (or Wald / bootstrap via method).

Usage

tls(
  object,
  by = NULL,
  params = c("all", "z", "ctmax"),
  target_surv = "relative",
  lethal = FALSE,
  method = NULL,
  level = 0.95,
  nboot = 1000L,
  TC_rate_range = c(0.1, 1),
  seed = NULL
)

tls_z(object, ...)

tls_ctmax(object, ...)

tls_tcrit(object, ...)

Arguments

object

A freq_tls fit from fit_4pl() (or a bare profile_tls fit from fit_tls()).

by

Optional name for the grouping column in $summary; defaults to the fit's moderator (e.g. the ctmax/z/by factor).

params

"all" (z and CTmax, the default), "z", or "ctmax". This selects only the returned headline quantities; use tdt_parameter_table() or get_shape() for low, up, and k.

target_surv

Survival threshold for CTmax: "relative" (the curve midpoint, the default), "absolute" (50% survival), or a number in (0, 1) for an LTx. An absolute target must lie strictly between the fitted asymptotes for every reported group. Non-relative thresholds and lethal are derived per bootstrap replicate via extract_tdt().

lethal

If TRUE, also report T_crit (the damage-rate-floor critical temperature); uses the bootstrap path.

method

Interval method for the relative path: "profile" (default, from the fit's stored default), "wald", or "bootstrap". Absolute / lethal always use bootstrap.

level

Confidence level (default 0.95).

nboot, TC_rate_range, seed

Passed to extract_tdt() for the bootstrap path (absolute / LTx / lethal).

...

Passed from tls_z() / tls_ctmax() / tls_tcrit() to tls().

Value

A tls object: a list with $summary (a tibble of [<group>,] quantity, median, lower, upper) and $meta.

See also

fit_4pl(), tls_z(), tls_ctmax(), confint.profile_tls()

Examples

raw <- simulate_tls(family = "binomial", CTmax = 36, z = 4, seed = 1)
dat <- standardize_data(
  raw, temp = "temp", duration = "duration",
  n_total = "total", n_surv = "survived"
)
fit <- fit_4pl(
  dat, family = "binomial", t_ref = 1, method = "wald", quiet = TRUE
)
tls(fit)
#> <tls> relative threshold; quantities: z, CTmax (wald intervals)
#> # A tibble: 2 × 4
#>   quantity median lower upper
#>   <chr>     <dbl> <dbl> <dbl>
#> 1 CTmax     35.9  35.7  36.1 
#> 2 z          4.00  3.64  4.40
tls_z(fit)
#> <tls> relative threshold; quantities: z (wald intervals)
#> # A tibble: 1 × 4
#>   quantity median lower upper
#>   <chr>     <dbl> <dbl> <dbl>
#> 1 z          4.00  3.64  4.40
tls_ctmax(fit)
#> <tls> relative threshold; quantities: CTmax (wald intervals)
#> # A tibble: 1 × 4
#>   quantity median lower upper
#>   <chr>     <dbl> <dbl> <dbl>
#> 1 CTmax      35.9  35.7  36.1